However, reduced-complexity detection algorithms usually undermine the error performance.
For instance, there are mainly two different aspects that may contribute to the performance loss if we apply the SISO algorithm in [1]} for FTN detection.
Firstly, due to the complexity limitation, the SISO algorithm can only consider a limited number of ISI taps.
Note that the number of ISI taps of FTN signaling can be infinite in theory [2]}. Therefore,
the unconsidered residual ISI will degrade the error performance of the system.
Secondly, the FG generated from the Ungerboeck observation model in [1]} has shortest cycles of length-6 and the cycles may accumulate the correlation between the messages during the iterations of detection. This accumulated correlation is difficult to be predicted by mathematical models and can undermine the error performance.
In light of these two aspects, we consider to utilize a neural network (NN) to compensate for the performance loss of the SISO algorithm for FTN detection.
